Introduction to Digital Twins in Manufacturing
Course provided by Model Institute of Engineering & Technology
4 modules
Explore the fundamentals of Cyber Physical Systems
4.5 Level NCrF
National Credit Framework
30 Hours
Flexible Learning
Beginner Level
No prior experience required
Nano Credit Course
01 Credit
Course Overview
This course introduces learners to the concept of Digital Twins and their transformative role in modern manufacturing. Through Siemens-led case studies, participants explore real-world applications, from product design and process optimization to predictive maintenance. The curriculum emphasizes strategy, architecture, and business impact, equipping learners with the knowledge to integrate Digital Twin technology into industrial ecosystems for enhanced efficiency, innovation, and decision-making.
Key Learning Highlights
- Understanding the concept of Digital Twins and their role in Industry 4.0.
- Exploring the business value, ROI, and organizational factors driving adoption.
- Learning about IT systems, data infrastructure, and integration needs for deployment.
- Analyzing real-world case studies and industry use cases of Digital Twin technology.
- Assessing organizational readiness for Digital Twin implementation in manufacturing and enterprises.
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
Define Digital Twins and explain their significance in Industry 4.0 ecosystems.
Analyze the business impact and organizational considerations in adopting Digital Twins.
Describe the IT infrastructure and data requirements essential for deployment.
Examine real-world case studies to connect theory with industry practice.
Assess the readiness of an enterprise or manufacturing unit for Digital Twin adoption.
Master the course with just 4 Modules
The course begins with the fundamentals of Digital Twins, introducing their history, types, and connections with IoT, AI, and Industry 4.0. Learners then explore the business value and adoption strategy, examining benefits such as predictive maintenance, efficiency gains, ROI analysis, and organizational readiness. The next module focuses on architecture and infrastructure, highlighting the role of sensors, cloud and edge computing, IT/OT integration, and cybersecurity considerations. Finally, students analyze industry applications and case studies across manufacturing, automotive, and energy sectors, with real-world insights from Siemens and other global leaders, while addressing challenges in scaling Digital Twin solutions.
Fundamentals of Digital Twins
- Define Digital Twins, their history, and evolution in modern industry.
- Differentiate between product, production, and performance twins.
- Understand the relationship of Digital Twins with IoT, AI, and Industry 4.0.
Business Value and Adoption Strategy
- Explore benefits such as predictive maintenance, efficiency, and quality improvement.
- Conduct ROI and cost-benefit analysis for Digital Twin projects.
- Examine organizational readiness, change management, and adoption challenges.
Digital Twin Architecture and Infrastructure
- Learn the role of sensors, data acquisition, cloud, and edge computing.
- Understand IT/OT integration and data flow in Digital Twin systems.
- Address cybersecurity, interoperability, and infrastructure concerns.
Industry Applications and Case Studies
Study applications of Digital Twins in manufacturing, automotive, and energy sectors.
Analyze real-world case studies from Siemens and industry leaders.
Identify challenges in implementation, scaling, and enterprise integration.
Roles
Digital Twin Engineer
Industry 4.0 Specialist
IoT Solutions Architect
Smart Manufacturing Engineer
Data & Systems Integration Specialist
